Extended Data Figure 9

Introduction

Load libraries

library(Seurat)
library(canceRbits)
library(dplyr)
library(patchwork)
library(DT)
library(SCpubr)
library(tibble)
library(dittoSeq)
library(scRepertoire)
library(reshape2)
library(viridis)
library(tidyr)

library(grDevices)
library(ggpubr)
library(ggplot2)
library(ggnewscale)

library(RColorBrewer)
library(scales)

library(enrichplot)
library(clusterProfiler)
library("org.Hs.eg.db")
library(DOSE)
library(msigdbr)
library(stringr)

Load single cell RNA-Seq data

# Load seurat object containing single cell pre-processed and annotated data, in the metadata folder

srat <- readRDS(params$path_to_data)
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))

colors

colors_B <- c(
  "34" = "#ae017e",
  "7"  =  "#377eb8",
  "21" =  "#f781bf",  
  "38" = "#fed976",
  "15" = "#a6cee3" ,
  "22" = "#e31a1c"   ,
   "3" = "#9e9ac8",
 "4"   = "#fdb462" ,
 "24"  = "#b3de69"
)


colors_clonotype = c("Small (1e-04 < X <= 0.001)"   = "#8c6bb1",
           "Medium (0.001 < X <= 0.01)"   = "#41b6c4",
           "Large (0.01 < X <= 0.1)"      = "#fec44f",
           "Hyperexpanded (0.1 < X <= 1)" = "#ce1256"
)

s   <- subset(srat, subset = anno_l1 %in% c("B cells","Plasma cells"))

s@meta.data$seurat_clusters <- factor(s@meta.data$seurat_clusters, levels=c("34","7", "21",  "38",
                                                                             "15", "22","3", "4", "24"))

Extended Data Figure 9a

df <- s@meta.data %>%
    mutate_if(sapply(s@meta.data, is.character), as.factor)  %>% 
    group_by( Pathological.Response, patient, seurat_clusters,    .drop = FALSE)%>% 
  summarise(Nb = n()) %>%
  mutate(C = sum(Nb)) %>%
  mutate(percent = Nb/C*100) %>%
  filter(percent != "NaN") %>%
  arrange(Pathological.Response, percent)




tmp <- df[df$seurat_clusters %in% c("34"),]


p1 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#dd3497", "#ae017e"), 
         point.size = 2, 
         xlab = "", 
         ylab = "Percentage (%)") + 
  ylim(c(0,90)) +
   ggtitle("C34") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 



tmp <- df[df$seurat_clusters %in% c("7"),]


p2 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#1d91c0", "#253494"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C7") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("21"),]


p3 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" ,  
         
         palette= c("white", "#fde0dd", "#fa9fb5"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C21") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("38"),]


p4 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#ffffcc", "yellow"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") +
  ylim(c(0,90)) +
   ggtitle("C38") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("15"),]


p5 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#deebf7", "#9ecae1"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C15") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("22"),]


p6 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#fc9272", "#ef3b2c"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C22") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("3"),]


p7 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#decbe4", "#c2a5cf"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C3") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("4"),]


p8 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#fee0b6", "#fec44f"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C4") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("24"),]


p9 <- ggpaired(tmp, 
         x = "Pathological.Response", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "Pathological.Response" , 
         palette= c("white", "#b8e186", "#7fbc41"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C24") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.format", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

p <- p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 + plot_layout(nrow = 1, guides = "collect")
p

DT::datatable(rbind(p1$data, p2$data, p3$data, p4$data, p5$data, p6$data, p7$data, p8$data, p9$data), 
              caption = ("Extended Data Figure 9A"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

s$WHO <- factor(s$WHO, levels = c("CR","PR","SD"))
df <- s@meta.data %>%
    mutate_if(sapply(s@meta.data, is.character), as.factor)  %>% 
    group_by( WHO, patient, seurat_clusters,    .drop = FALSE)%>% 
  summarise(Nb = n()) %>%
  mutate(C = sum(Nb)) %>%
  mutate(percent = Nb/C*100) %>%
  filter(percent != "NaN") %>%
  arrange(WHO, percent)




tmp <- df[df$seurat_clusters %in% c("34"),]


p1 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#dd3497", "#ae017e"), 
         point.size = 2, 
         xlab = "", 
         ylab = "Percentage (%)") + 
  ylim(c(0,90)) +
   ggtitle("C34") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 



tmp <- df[df$seurat_clusters %in% c("7"),]


p2 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#1d91c0", "#253494"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C7") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("21"),]


p3 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" ,  
         
         palette= c("white", "#fde0dd", "#fa9fb5"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C21") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("38"),]


p4 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#ffffcc", "yellow"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") +
  ylim(c(0,90)) +
   ggtitle("C38") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("15"),]


p5 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#deebf7", "#9ecae1"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
   ggtitle("C15") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("22"),]


p6 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#fc9272", "#ef3b2c"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C22") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("3"),]


p7 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#decbe4", "#c2a5cf"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C3") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

tmp <- df[df$seurat_clusters %in% c("4"),]


p8 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#fee0b6", "#fec44f"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C4") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 


tmp <- df[df$seurat_clusters %in% c("24"),]


p9 <- ggpaired(tmp, 
         x = "WHO", 
         y = "percent", 
         id="patient", 
         color = "black", 
         fill = "WHO" , 
         palette= c("white", "#b8e186", "#7fbc41"), 
         point.size = 2, 
         xlab = "", 
         ylab = "") + 
  ylim(c(0,90)) +
  ggtitle("C24") + NoLegend() +
  stat_compare_means( method = "wilcox.test", 
                      paired = FALSE, 
                      label.y = 80, 
                      hide.ns = FALSE, 
                      label = "p.signif", 
                      label.x.npc = "center", 
                      bracket.size = 1, 
                      comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")), 
                      size = 8)  +
  theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90,  vjust=0.5)) 

p <- p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 + plot_layout(nrow = 1, guides = "collect")
p

DT::datatable(rbind(p1$data, p2$data, p3$data, p4$data, p5$data, p6$data, p7$data, p8$data, p9$data), 
              caption = ("Extended Data Figure 9A"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

Extended Data Figure 9b

p1 <- Seurat::VlnPlot(s, 
                     features = "HLA-DRA", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p1 <- p1 + geom_boxplot(width=0.3, fill="white")

f1 <- SCpubr::do_FeaturePlot(s, 
                     features = "HLA-DRA", legend.position = "right", legend.length = 4)


p2 <- Seurat::VlnPlot(s, 
                     features = "GZMK", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p2 <- p2 + geom_boxplot(width=0.3, fill="white")

f2 <- SCpubr::do_FeaturePlot(s, 
                     features = "GZMK", legend.position = "right", legend.length = 4)



p3 <- Seurat::VlnPlot(s, 
                     features = "CD27", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p3 <- p3 + geom_boxplot(width=0.3, fill="white")

f3 <- SCpubr::do_FeaturePlot(s, 
                     features = "CD27", legend.position = "right", legend.length = 4)


p4 <- Seurat::VlnPlot(s, 
                     features = "XBP1", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p4 <- p4 + geom_boxplot(width=0.3, fill="white")

f4 <- SCpubr::do_FeaturePlot(s, 
                     features = "XBP1", legend.position = "right", legend.length = 4)



c1 <- f1+p1  + plot_layout(ncol = 2,  widths   = c(1,2)) 
c2 <- f2+p2  + plot_layout(ncol = 2,  widths   = c(1,2))
c3 <- f3+p3  + plot_layout(ncol = 2,  widths   = c(1,2))
c4 <- f4+p4  + plot_layout(ncol = 2,  widths   = c(1,2))

c1

DT::datatable(f1$data, 
              caption = ("EDFigure 9Ba_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

c2

DT::datatable(f2$data, 
              caption = ("EDFigure 9Bb_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

c3

DT::datatable(f3$data, 
              caption = ("EDFigure 9Bc_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

c4

DT::datatable(f4$data, 
              caption = ("EDFigure 9Bd_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p1$data, 
              caption = ("EDFigure 9Ba_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p2$data, 
              caption = ("EDFigure 9Bb_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p3$data, 
              caption = ("EDFigure 9Bc_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p4$data, 
              caption = ("EDFigure 9Bd_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

Extended Data Figure 9C

p1 <- Seurat::VlnPlot(s, 
                     features = "PRF1", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p1 <- p1 + geom_boxplot(width=0.3, fill="white")

f1 <- SCpubr::do_FeaturePlot(s, 
                     features = "PRF1", legend.position = "right", legend.length = 4)


p2 <- Seurat::VlnPlot(s, 
                     features = "IL10", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p2 <- p2 + geom_boxplot(width=0.3, fill="white")

f2 <- SCpubr::do_FeaturePlot(s, 
                     features = "IL10", legend.position = "right", legend.length = 4)



p3 <- Seurat::VlnPlot(s, 
                     features = "TGFB1", 
                     group.by = "seurat_clusters", 
                     pt.size = 0,  
                     cols = colors_B) & NoLegend() 
p3 <- p3 + geom_boxplot(width=0.3, fill="white")

f3 <- SCpubr::do_FeaturePlot(s, 
                     features = "TGFB1", legend.position = "right", legend.length = 4)





c1 <- f1+p1  + plot_layout(ncol = 1,  heights =  c(1,1)) 
c2 <- f2+p2  + plot_layout(ncol = 1,  heights = c(1,1))
c3 <- f3+p3  + plot_layout(ncol = 1,  heights = c(1,1))

c1

DT::datatable(f1$data, 
              caption = ("ExtendedData_Figure9Ca_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p1$data, 
              caption = ("ExtendedData_Figure9Ca_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

c2

DT::datatable(f2$data, 
              caption = ("ExtendedData_Figure9Cb_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p2$data, 
              caption = ("ExtendedData_Figure9Cb_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

c3

DT::datatable(f3$data, 
              caption = ("ExtendedData_Figure9Cc_FeaturePlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

DT::datatable(p3$data, 
              caption = ("ExtendedData_Figure9Cc_VlnPlot"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))